Zobrazeno 1 - 10
of 332
pro vyhledávání: '"Newey, Whitney K."'
In this paper we address the problem of bias in machine learning of parameters following covariate shifts. Covariate shift occurs when the distribution of input features change between the training and deployment stages. Regularization and model sele
Externí odkaz:
http://arxiv.org/abs/2307.04527
Bias correction can often improve the finite sample performance of estimators. We show that the choice of bias correction method has no effect on the higher-order variance of semiparametrically efficient parametric estimators, so long as the estimate
Externí odkaz:
http://arxiv.org/abs/2207.09943
Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce
Externí odkaz:
http://arxiv.org/abs/2110.03031
Debiased machine learning is a meta algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e. scalar summaries, of machine learning algorithms. For example, an analyst may desire the confidence in
Externí odkaz:
http://arxiv.org/abs/2105.15197
A variety of interesting parameters may depend on high dimensional regressions. Machine learning can be used to estimate such parameters. However estimators based on machine learners can be severely biased by regularization and/or model selection. De
Externí odkaz:
http://arxiv.org/abs/2104.14737
Autor:
Newey, Whitney K., Stouli, Sami
Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable repres
Externí odkaz:
http://arxiv.org/abs/2009.02314
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as r
Externí odkaz:
http://arxiv.org/abs/1912.12213
The drift diffusion model (DDM) is a model of sequential sampling with diffusion (Brownian) signals, where the decision maker accumulates evidence until the process hits a stopping boundary, and then stops and chooses the alternative that corresponds
Externí odkaz:
http://arxiv.org/abs/1908.05824
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Newey, Whitney K., Stouli, Sami
Multidimensional heterogeneity and endogeneity are important features of a wide class of econometric models. We consider heterogenous coefficients models where the outcome is a linear combination of known functions of treatment and heterogenous coeff
Externí odkaz:
http://arxiv.org/abs/1811.09837